Overview

Dataset statistics

Number of variables29
Number of observations19026
Missing cells244163
Missing cells (%)44.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory10.3 MiB
Average record size in memory569.0 B

Variable types

Categorical10
Numeric14
Unsupported5

Alerts

Change_Temperature_Time has constant value "180.0" Constant
Data has a high cardinality: 357 distinct values High cardinality
Material has a high cardinality: 200 distinct values High cardinality
Product Code has a high cardinality: 1429 distinct values High cardinality
Batch_ID has a high cardinality: 1359 distinct values High cardinality
Number_of_Case has a high cardinality: 62 distinct values High cardinality
Number_of_Boxes has a high cardinality: 64 distinct values High cardinality
Material_Type is highly correlated with temperature and 1 other fieldsHigh correlation
temperature is highly correlated with Material_Type and 1 other fieldsHigh correlation
Speed is highly correlated with Material_Type and 1 other fieldsHigh correlation
weight_kg is highly correlated with unit_weight_g and 3 other fieldsHigh correlation
unit_weight_g is highly correlated with weight_kgHigh correlation
Process_Time is highly correlated with weight_kg and 2 other fieldsHigh correlation
Waiting_Time is highly correlated with Working_Hours and 1 other fieldsHigh correlation
Holding_Time is highly correlated with Working_Hours and 1 other fieldsHigh correlation
Working_Hours is highly correlated with weight_kg and 4 other fieldsHigh correlation
Total_Working_Hours is highly correlated with weight_kg and 4 other fieldsHigh correlation
weight_kg is highly correlated with Process_Time and 2 other fieldsHigh correlation
Process_Time is highly correlated with weight_kg and 2 other fieldsHigh correlation
Waiting_Time is highly correlated with Working_Hours and 1 other fieldsHigh correlation
Holding_Time is highly correlated with Working_Hours and 1 other fieldsHigh correlation
Working_Hours is highly correlated with weight_kg and 4 other fieldsHigh correlation
Total_Working_Hours is highly correlated with weight_kg and 4 other fieldsHigh correlation
Material_Type is highly correlated with temperature and 1 other fieldsHigh correlation
temperature is highly correlated with Material_Type and 1 other fieldsHigh correlation
Speed is highly correlated with Material_Type and 1 other fieldsHigh correlation
weight_kg is highly correlated with Process_Time and 2 other fieldsHigh correlation
Process_Time is highly correlated with weight_kg and 2 other fieldsHigh correlation
Waiting_Time is highly correlated with Working_Hours and 1 other fieldsHigh correlation
Holding_Time is highly correlated with Working_Hours and 1 other fieldsHigh correlation
Working_Hours is highly correlated with weight_kg and 4 other fieldsHigh correlation
Total_Working_Hours is highly correlated with weight_kg and 4 other fieldsHigh correlation
Tool is highly correlated with Material_Type and 5 other fieldsHigh correlation
Material_Type is highly correlated with Tool and 3 other fieldsHigh correlation
Speed is highly correlated with Tool and 2 other fieldsHigh correlation
weight_kg is highly correlated with Number_of_Boxes and 3 other fieldsHigh correlation
Process_Time is highly correlated with Number_of_Boxes and 2 other fieldsHigh correlation
Waiting_Time is highly correlated with Number_of_Boxes and 2 other fieldsHigh correlation
Holding_Time is highly correlated with Form_Number and 2 other fieldsHigh correlation
Form_Number is highly correlated with Tool and 2 other fieldsHigh correlation
Number_of_Case is highly correlated with Tool and 1 other fieldsHigh correlation
Number_of_Boxes is highly correlated with Tool and 5 other fieldsHigh correlation
Sintered_State is highly correlated with Tool and 2 other fieldsHigh correlation
Working_Hours is highly correlated with weight_kg and 5 other fieldsHigh correlation
Total_Working_Hours is highly correlated with weight_kg and 5 other fieldsHigh correlation
Material_Type has 4658 (24.5%) missing values Missing
Material has 5601 (29.4%) missing values Missing
Product Code has 4330 (22.8%) missing values Missing
Batch_ID has 5066 (26.6%) missing values Missing
temperature has 4331 (22.8%) missing values Missing
Speed has 4348 (22.9%) missing values Missing
quench has 19026 (100.0%) missing values Missing
rate has 19026 (100.0%) missing values Missing
Rapid dewaxing has 19026 (100.0%) missing values Missing
weight_kg has 5157 (27.1%) missing values Missing
Process_Time has 4308 (22.6%) missing values Missing
Waiting_Time has 14841 (78.0%) missing values Missing
Holding_Time has 18964 (99.7%) missing values Missing
Change_Temperature_Time has 19025 (> 99.9%) missing values Missing
Furnace_Maintenance_Time has 19026 (100.0%) missing values Missing
Form_Number has 15718 (82.6%) missing values Missing
Affirmant has 696 (3.7%) missing values Missing
Filler has 19026 (100.0%) missing values Missing
Recipient has 655 (3.4%) missing values Missing
Number_of_Case has 17709 (93.1%) missing values Missing
Number_of_Boxes has 5356 (28.2%) missing values Missing
Sintered_State has 18247 (95.9%) missing values Missing
temperature is highly skewed (γ1 = 48.61856227) Skewed
quench is an unsupported type, check if it needs cleaning or further analysis Unsupported
rate is an unsupported type, check if it needs cleaning or further analysis Unsupported
Rapid dewaxing is an unsupported type, check if it needs cleaning or further analysis Unsupported
Furnace_Maintenance_Time is an unsupported type, check if it needs cleaning or further analysis Unsupported
Filler is an unsupported type, check if it needs cleaning or further analysis Unsupported
Qty has 5130 (27.0%) zeros Zeros

Reproduction

Analysis started2022-04-24 15:06:57.346559
Analysis finished2022-04-24 15:07:27.767500
Duration30.42 seconds
Software versionpandas-profiling v3.1.0
Download configurationconfig.json

Variables

Data
Categorical

HIGH CARDINALITY

Distinct357
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
7/29
 
79
7/13
 
73
7/17
 
70
6/18
 
70
7/30
 
70
Other values (352)
18664 

Length

Max length5
Median length4
Mean length3.970146116
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1/1
2nd row1/1
3rd row1/1
4th row1/1
5th row1/1

Common Values

ValueCountFrequency (%)
7/2979
 
0.4%
7/1373
 
0.4%
7/1770
 
0.4%
6/1870
 
0.4%
7/3070
 
0.4%
12/3169
 
0.4%
10/768
 
0.4%
7/2767
 
0.4%
7/2867
 
0.4%
8/2667
 
0.4%
Other values (347)18326
96.3%

Length

2022-04-24T23:07:27.861225image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
7/2979
 
0.4%
7/1373
 
0.4%
7/1770
 
0.4%
6/1870
 
0.4%
7/3070
 
0.4%
12/3169
 
0.4%
10/768
 
0.4%
8/2667
 
0.4%
7/2867
 
0.4%
7/2767
 
0.4%
Other values (347)18326
96.3%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Shift
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
C
9559 
A
9463 
B
 
2
CD
 
1
C
 
1

Length

Max length2
Median length1
Mean length1.000105119
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st rowC
2nd rowC
3rd rowA
4th rowA
5th rowA

Common Values

ValueCountFrequency (%)
C9559
50.2%
A9463
49.7%
B2
 
< 0.1%
CD1
 
< 0.1%
C1
 
< 0.1%

Length

2022-04-24T23:07:27.987163image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-24T23:07:28.072935image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
c9560
50.2%
a9463
49.7%
b2
 
< 0.1%
cd1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Tool
Real number (ℝ≥0)

HIGH CORRELATION

Distinct9
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.184011353
Minimum1
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size148.8 KiB
2022-04-24T23:07:28.162696image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median4
Q36
95-th percentile8
Maximum9
Range8
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.46376928
Coefficient of variation (CV)0.5888533927
Kurtosis-1.221500607
Mean4.184011353
Median Absolute Deviation (MAD)2
Skewness0.2527713456
Sum79605
Variance6.070159067
MonotonicityNot monotonic
2022-04-24T23:07:28.263968image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
13546
18.6%
22780
14.6%
42233
11.7%
32233
11.7%
82051
10.8%
72022
10.6%
51956
10.3%
61773
9.3%
9432
 
2.3%
ValueCountFrequency (%)
13546
18.6%
22780
14.6%
32233
11.7%
42233
11.7%
51956
10.3%
61773
9.3%
72022
10.6%
82051
10.8%
9432
 
2.3%
ValueCountFrequency (%)
9432
 
2.3%
82051
10.8%
72022
10.6%
61773
9.3%
51956
10.3%
42233
11.7%
32233
11.7%
22780
14.6%
13546
18.6%

Material_Type
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct4
Distinct (%)< 0.1%
Missing4658
Missing (%)24.5%
Memory size1024.0 KiB
1.0
12040 
3.0
2296 
2.0
 
28
24.0
 
4

Length

Max length4
Median length3
Mean length3.000278396
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.012040
63.3%
3.02296
 
12.1%
2.028
 
0.1%
24.04
 
< 0.1%
(Missing)4658
 
24.5%

Length

2022-04-24T23:07:28.388136image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-24T23:07:28.465944image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
1.012040
83.8%
3.02296
 
16.0%
2.028
 
0.2%
24.04
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Material
Categorical

HIGH CARDINALITY
MISSING

Distinct200
Distinct (%)1.5%
Missing5601
Missing (%)29.4%
Memory size963.3 KiB
15
3269 
20
930 
29
841 
24
770 
5-3
 
575
Other values (195)
7040 

Length

Max length13
Median length2
Mean length2.90972067
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique37 ?
Unique (%)0.3%

Sample

1st row25
2nd row15-1
3rd row15-1
4th row5-7
5th row5-3

Common Values

ValueCountFrequency (%)
153269
17.2%
20930
 
4.9%
29841
 
4.4%
24770
 
4.0%
5-3575
 
3.0%
15-5545
 
2.9%
13390
 
2.0%
5-2367
 
1.9%
20-2322
 
1.7%
30272
 
1.4%
Other values (190)5144
27.0%
(Missing)5601
29.4%

Length

2022-04-24T23:07:28.566637image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
153269
24.3%
20930
 
6.9%
29841
 
6.3%
24770
 
5.7%
5-3575
 
4.3%
15-5557
 
4.1%
13390
 
2.9%
5-2367
 
2.7%
20-2322
 
2.4%
30273
 
2.0%
Other values (185)5149
38.3%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Product Code
Categorical

HIGH CARDINALITY
MISSING

Distinct1429
Distinct (%)9.7%
Missing4330
Missing (%)22.8%
Memory size1012.6 KiB
4274
 
1066
5469
 
815
6536
 
768
3285
 
337
6508
 
287
Other values (1424)
11423 

Length

Max length7
Median length4
Mean length4.117787153
Min length4

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique337 ?
Unique (%)2.3%

Sample

1st row6808
2nd row8005
3rd row8005
4th row4454
5th row4666

Common Values

ValueCountFrequency (%)
42741066
 
5.6%
5469815
 
4.3%
6536768
 
4.0%
3285337
 
1.8%
6508287
 
1.5%
6538279
 
1.5%
2610268
 
1.4%
4005252
 
1.3%
4280230
 
1.2%
6295215
 
1.1%
Other values (1419)10179
53.5%
(Missing)4330
22.8%

Length

2022-04-24T23:07:28.692301image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
42741066
 
7.3%
5469815
 
5.5%
6536768
 
5.2%
3285337
 
2.3%
6508287
 
2.0%
6538279
 
1.9%
2610268
 
1.8%
4005252
 
1.7%
4280230
 
1.6%
6295215
 
1.5%
Other values (1419)10179
69.3%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Batch_ID
Categorical

HIGH CARDINALITY
MISSING

Distinct1359
Distinct (%)9.7%
Missing5066
Missing (%)26.6%
Memory size1.0 MiB
¸Õ
 
371
210221A
 
92
210330A
 
69
210417B
 
68
210409B
 
63
Other values (1354)
13297 

Length

Max length9
Median length7
Mean length6.773280802
Min length2

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique259 ?
Unique (%)1.9%

Sample

1st row201230A
2nd row201231A
3rd row201231A
4th row201230A
5th row201231A

Common Values

ValueCountFrequency (%)
¸Õ371
 
1.9%
210221A92
 
0.5%
210330A69
 
0.4%
210417B68
 
0.4%
210409B63
 
0.3%
210413B62
 
0.3%
210501B57
 
0.3%
211113A54
 
0.3%
210825C50
 
0.3%
210626B48
 
0.3%
Other values (1349)13026
68.5%
(Missing)5066
 
26.6%

Length

2022-04-24T23:07:28.824983image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
¸õ372
 
2.7%
210221a92
 
0.7%
210330a69
 
0.5%
210417b68
 
0.5%
210409b63
 
0.5%
210413b62
 
0.4%
210501b57
 
0.4%
211113a54
 
0.4%
210825c50
 
0.4%
210626b48
 
0.3%
Other values (1341)13026
93.3%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

temperature
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
MISSING
SKEWED

Distinct35
Distinct (%)0.2%
Missing4331
Missing (%)22.8%
Infinite0
Infinite (%)0.0%
Mean1123.898632
Minimum50
Maximum11220
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size148.8 KiB
2022-04-24T23:07:29.056702image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum50
5-th percentile1120
Q11120
median1120
Q31120
95-th percentile1150
Maximum11220
Range11170
Interquartile range (IQR)0

Descriptive statistics

Standard deviation192.395128
Coefficient of variation (CV)0.1711854811
Kurtosis2559.557907
Mean1123.898632
Median Absolute Deviation (MAD)0
Skewness48.61856227
Sum16515690.4
Variance37015.88528
MonotonicityNot monotonic
2022-04-24T23:07:29.183498image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=35)
ValueCountFrequency (%)
112011802
62.0%
11501789
 
9.4%
1135233
 
1.2%
1164210
 
1.1%
1130129
 
0.7%
111098
 
0.5%
113496
 
0.5%
114069
 
0.4%
105061
 
0.3%
100035
 
0.2%
Other values (25)173
 
0.9%
(Missing)4331
 
22.8%
ValueCountFrequency (%)
508
 
< 0.1%
609
 
< 0.1%
1102
 
< 0.1%
1121
 
< 0.1%
113.41
 
< 0.1%
6001
 
< 0.1%
70012
0.1%
7607
 
< 0.1%
80027
0.1%
8403
 
< 0.1%
ValueCountFrequency (%)
112204
 
< 0.1%
111201
 
< 0.1%
1164210
 
1.1%
11604
 
< 0.1%
11553
 
< 0.1%
11501789
9.4%
11442
 
< 0.1%
114069
 
0.4%
1135233
 
1.2%
113496
 
0.5%

Speed
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct21
Distinct (%)0.1%
Missing4348
Missing (%)22.9%
Infinite0
Infinite (%)0.0%
Mean59.26890585
Minimum25
Maximum90
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size148.8 KiB
2022-04-24T23:07:29.306172image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum25
5-th percentile50
Q160
median60
Q360
95-th percentile70
Maximum90
Range65
Interquartile range (IQR)0

Descriptive statistics

Standard deviation6.03598952
Coefficient of variation (CV)0.1018407449
Kurtosis5.808293575
Mean59.26890585
Median Absolute Deviation (MAD)0
Skewness0.7073004766
Sum869949
Variance36.43316949
MonotonicityNot monotonic
2022-04-24T23:07:29.408860image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
6011265
59.2%
501963
 
10.3%
80578
 
3.0%
55348
 
1.8%
70263
 
1.4%
5395
 
0.5%
4045
 
0.2%
3326
 
0.1%
4622
 
0.1%
6517
 
0.1%
Other values (11)56
 
0.3%
(Missing)4348
 
22.9%
ValueCountFrequency (%)
258
 
< 0.1%
3013
 
0.1%
3326
 
0.1%
348
 
< 0.1%
4045
 
0.2%
435
 
< 0.1%
456
 
< 0.1%
4622
 
0.1%
489
 
< 0.1%
501963
10.3%
ValueCountFrequency (%)
903
 
< 0.1%
80578
 
3.0%
70263
 
1.4%
6517
 
0.1%
641
 
< 0.1%
631
 
< 0.1%
621
 
< 0.1%
611
 
< 0.1%
6011265
59.2%
55348
 
1.8%

quench
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing19026
Missing (%)100.0%
Memory size148.8 KiB

rate
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing19026
Missing (%)100.0%
Memory size148.8 KiB

Rapid dewaxing
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing19026
Missing (%)100.0%
Memory size148.8 KiB

weight_kg
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct2165
Distinct (%)15.6%
Missing5157
Missing (%)27.1%
Infinite0
Infinite (%)0.0%
Mean129.1806958
Minimum0
Maximum1629
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size148.8 KiB
2022-04-24T23:07:29.539896image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile13.5
Q140
median82
Q3177.7
95-th percentile388
Maximum1629
Range1629
Interquartile range (IQR)137.7

Descriptive statistics

Standard deviation128.7056065
Coefficient of variation (CV)0.996322289
Kurtosis5.176400403
Mean129.1806958
Median Absolute Deviation (MAD)54
Skewness1.958436042
Sum1791607.07
Variance16565.13315
MonotonicityNot monotonic
2022-04-24T23:07:29.693510image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20530
 
2.8%
40468
 
2.5%
60399
 
2.1%
80259
 
1.4%
100214
 
1.1%
120165
 
0.9%
140157
 
0.8%
10122
 
0.6%
51.8119
 
0.6%
160118
 
0.6%
Other values (2155)11318
59.5%
(Missing)5157
27.1%
ValueCountFrequency (%)
01
 
< 0.1%
0.51
 
< 0.1%
0.72
 
< 0.1%
0.82
 
< 0.1%
14
< 0.1%
1.22
 
< 0.1%
1.31
 
< 0.1%
1.55
< 0.1%
1.62
 
< 0.1%
1.71
 
< 0.1%
ValueCountFrequency (%)
16291
 
< 0.1%
898.91
 
< 0.1%
862.41
 
< 0.1%
794.51
 
< 0.1%
7801
 
< 0.1%
739.210
 
0.1%
709.51
 
< 0.1%
707.71
 
< 0.1%
707.410
 
0.1%
691.626
0.1%

unit_weight_g
Real number (ℝ≥0)

HIGH CORRELATION

Distinct1391
Distinct (%)7.3%
Missing23
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean139.6020938
Minimum0.09
Maximum4517
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size148.8 KiB
2022-04-24T23:07:29.854037image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0.09
5-th percentile1
Q11
median22.05
Q3271.5
95-th percentile540
Maximum4517
Range4516.91
Interquartile range (IQR)270.5

Descriptive statistics

Standard deviation199.0073181
Coefficient of variation (CV)1.425532474
Kurtosis17.94699145
Mean139.6020938
Median Absolute Deviation (MAD)21.05
Skewness2.073775351
Sum2652858.588
Variance39603.91266
MonotonicityNot monotonic
2022-04-24T23:07:30.000116image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
14676
24.6%
5401003
 
5.3%
440761
 
4.0%
393742
 
3.9%
313.7346
 
1.8%
4.48332
 
1.7%
422279
 
1.5%
4.6256
 
1.3%
89197
 
1.0%
639151
 
0.8%
Other values (1381)10260
53.9%
ValueCountFrequency (%)
0.093
 
< 0.1%
0.123
 
< 0.1%
0.154
 
< 0.1%
0.256
 
< 0.1%
0.2611
0.1%
0.275
 
< 0.1%
0.2817
0.1%
0.32
 
< 0.1%
0.311
 
< 0.1%
0.334
 
< 0.1%
ValueCountFrequency (%)
45171
 
< 0.1%
36891
 
< 0.1%
11631
 
< 0.1%
10102
 
< 0.1%
10022
 
< 0.1%
10002
 
< 0.1%
9812
 
< 0.1%
972.63
< 0.1%
8752
 
< 0.1%
874.55
< 0.1%

Qty
Real number (ℝ≥0)

ZEROS

Distinct7136
Distinct (%)37.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3336.355441
Minimum0
Maximum553571.4286
Zeros5130
Zeros (%)27.0%
Negative0
Negative (%)0.0%
Memory size148.8 KiB
2022-04-24T23:07:30.141128image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median611.944056
Q32733.269263
95-th percentile16807.3286
Maximum553571.4286
Range553571.4286
Interquartile range (IQR)2733.269263

Descriptive statistics

Standard deviation8376.277344
Coefficient of variation (CV)2.510607006
Kurtosis1002.525845
Mean3336.355441
Median Absolute Deviation (MAD)611.944056
Skewness18.27410708
Sum63477498.63
Variance70162022.15
MonotonicityNot monotonic
2022-04-24T23:07:30.282752image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
05130
 
27.0%
800124
 
0.7%
95.92592593116
 
0.6%
12089
 
0.5%
71.8518518580
 
0.4%
160070
 
0.4%
60067
 
0.4%
9067
 
0.4%
224.881516656
 
0.3%
56055
 
0.3%
Other values (7126)13172
69.2%
ValueCountFrequency (%)
05130
27.0%
21
 
< 0.1%
31
 
< 0.1%
41
 
< 0.1%
61
 
< 0.1%
7.9629629632
 
< 0.1%
104
 
< 0.1%
10.925925931
 
< 0.1%
122
 
< 0.1%
12.56983241
 
< 0.1%
ValueCountFrequency (%)
553571.42861
< 0.1%
132558.13951
< 0.1%
122674.41861
< 0.1%
121428.57141
< 0.1%
1160001
< 0.1%
112903.22581
< 0.1%
1125001
< 0.1%
101382.48851
< 0.1%
93023.255811
< 0.1%
92105.263161
< 0.1%

Process_Time
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct94
Distinct (%)0.6%
Missing4308
Missing (%)22.6%
Infinite0
Infinite (%)0.0%
Mean202.9660959
Minimum5
Maximum480
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size148.8 KiB
2022-04-24T23:07:30.430850image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile30
Q190
median180
Q3240
95-th percentile480
Maximum480
Range475
Interquartile range (IQR)150

Descriptive statistics

Standard deviation139.2607303
Coefficient of variation (CV)0.6861280434
Kurtosis-0.4115739012
Mean202.9660959
Median Absolute Deviation (MAD)85
Skewness0.7417691164
Sum2987255
Variance19393.551
MonotonicityNot monotonic
2022-04-24T23:07:30.580093image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2402882
15.1%
4801828
 
9.6%
60967
 
5.1%
120834
 
4.4%
90678
 
3.6%
180665
 
3.5%
150517
 
2.7%
30500
 
2.6%
210319
 
1.7%
40291
 
1.5%
Other values (84)5237
27.5%
(Missing)4308
22.6%
ValueCountFrequency (%)
593
 
0.5%
10192
 
1.0%
1549
 
0.3%
20152
 
0.8%
2546
 
0.2%
30500
2.6%
3541
 
0.2%
40291
1.5%
4581
 
0.4%
50234
1.2%
ValueCountFrequency (%)
4801828
9.6%
4701
 
< 0.1%
4652
 
< 0.1%
46012
 
0.1%
4552
 
< 0.1%
45045
 
0.2%
4451
 
< 0.1%
44025
 
0.1%
4356
 
< 0.1%
43018
 
0.1%

Waiting_Time
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct79
Distinct (%)1.9%
Missing14841
Missing (%)78.0%
Infinite0
Infinite (%)0.0%
Mean298.9713262
Minimum10
Maximum720
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size148.8 KiB
2022-04-24T23:07:30.724709image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile60
Q1240
median240
Q3480
95-th percentile480
Maximum720
Range710
Interquartile range (IQR)240

Descriptive statistics

Standard deviation146.959868
Coefficient of variation (CV)0.4915517147
Kurtosis-1.207339039
Mean298.9713262
Median Absolute Deviation (MAD)120
Skewness0.05814542112
Sum1251195
Variance21597.20281
MonotonicityNot monotonic
2022-04-24T23:07:30.872484image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2401585
 
8.3%
4801433
 
7.5%
60133
 
0.7%
180116
 
0.6%
120115
 
0.6%
9085
 
0.4%
3073
 
0.4%
15072
 
0.4%
21055
 
0.3%
8029
 
0.2%
Other values (69)489
 
2.6%
(Missing)14841
78.0%
ValueCountFrequency (%)
106
 
< 0.1%
152
 
< 0.1%
2021
 
0.1%
255
 
< 0.1%
3073
0.4%
351
 
< 0.1%
4018
 
0.1%
454
 
< 0.1%
5021
 
0.1%
553
 
< 0.1%
ValueCountFrequency (%)
7204
 
< 0.1%
5701
 
< 0.1%
4801433
7.5%
4508
 
< 0.1%
4406
 
< 0.1%
4303
 
< 0.1%
42023
 
0.1%
4151
 
< 0.1%
4103
 
< 0.1%
4003
 
< 0.1%

Holding_Time
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct20
Distinct (%)32.3%
Missing18964
Missing (%)99.7%
Infinite0
Infinite (%)0.0%
Mean259.516129
Minimum20
Maximum480
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size148.8 KiB
2022-04-24T23:07:31.004131image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum20
5-th percentile41
Q1146.25
median240
Q3420
95-th percentile480
Maximum480
Range460
Interquartile range (IQR)273.75

Descriptive statistics

Standard deviation152.4787265
Coefficient of variation (CV)0.5875500958
Kurtosis-1.179696458
Mean259.516129
Median Absolute Deviation (MAD)120
Skewness0.2867041535
Sum16090
Variance23249.76203
MonotonicityNot monotonic
2022-04-24T23:07:31.227534image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
48015
 
0.1%
24013
 
0.1%
1205
 
< 0.1%
605
 
< 0.1%
1803
 
< 0.1%
2002
 
< 0.1%
1502
 
< 0.1%
2102
 
< 0.1%
3602
 
< 0.1%
302
 
< 0.1%
Other values (10)11
 
0.1%
(Missing)18964
99.7%
ValueCountFrequency (%)
201
 
< 0.1%
302
 
< 0.1%
401
 
< 0.1%
605
< 0.1%
1205
< 0.1%
1301
 
< 0.1%
1451
 
< 0.1%
1502
 
< 0.1%
1701
 
< 0.1%
1803
< 0.1%
ValueCountFrequency (%)
48015
0.1%
4202
 
< 0.1%
3602
 
< 0.1%
3301
 
< 0.1%
3001
 
< 0.1%
2701
 
< 0.1%
24013
0.1%
2102
 
< 0.1%
2002
 
< 0.1%
1851
 
< 0.1%

Change_Temperature_Time
Categorical

CONSTANT
MISSING
REJECTED

Distinct1
Distinct (%)100.0%
Missing19025
Missing (%)> 99.9%
Memory size743.3 KiB
180.0

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)100.0%

Sample

1st row180.0

Common Values

ValueCountFrequency (%)
180.01
 
< 0.1%
(Missing)19025
> 99.9%

Length

2022-04-24T23:07:31.337838image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-24T23:07:31.404186image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
180.01
100.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Furnace_Maintenance_Time
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing19026
Missing (%)100.0%
Memory size148.8 KiB

Form_Number
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct562
Distinct (%)17.0%
Missing15718
Missing (%)82.6%
Infinite0
Infinite (%)0.0%
Mean1319.492443
Minimum1
Maximum4415
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size148.8 KiB
2022-04-24T23:07:31.489182image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile29.35
Q1364
median629
Q32525
95-th percentile4282
Maximum4415
Range4414
Interquartile range (IQR)2161

Descriptive statistics

Standard deviation1253.793839
Coefficient of variation (CV)0.9502091851
Kurtosis-0.3834289233
Mean1319.492443
Median Absolute Deviation (MAD)413
Skewness0.8984607071
Sum4364881
Variance1571998.99
MonotonicityNot monotonic
2022-04-24T23:07:31.636692image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2858134
 
0.7%
37010
 
0.1%
58510
 
0.1%
61610
 
0.1%
104510
 
0.1%
32710
 
0.1%
95410
 
0.1%
60210
 
0.1%
233610
 
0.1%
36110
 
0.1%
Other values (552)3084
 
16.2%
(Missing)15718
82.6%
ValueCountFrequency (%)
16
< 0.1%
27
< 0.1%
37
< 0.1%
48
< 0.1%
59
< 0.1%
65
< 0.1%
77
< 0.1%
88
< 0.1%
96
< 0.1%
104
< 0.1%
ValueCountFrequency (%)
44157
< 0.1%
44144
 
< 0.1%
44134
 
< 0.1%
44124
 
< 0.1%
44114
 
< 0.1%
44107
< 0.1%
44092
 
< 0.1%
440810
0.1%
440710
0.1%
44062
 
< 0.1%

Affirmant
Real number (ℝ≥0)

MISSING

Distinct44
Distinct (%)0.2%
Missing696
Missing (%)3.7%
Infinite0
Infinite (%)0.0%
Mean250.2063501
Minimum0
Maximum3698
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size148.8 KiB
2022-04-24T23:07:31.788918image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile49
Q1248
median248
Q3325
95-th percentile398
Maximum3698
Range3698
Interquartile range (IQR)77

Descriptive statistics

Standard deviation149.3154731
Coefficient of variation (CV)0.5967693188
Kurtosis194.0862827
Mean250.2063501
Median Absolute Deviation (MAD)38
Skewness9.255249186
Sum4586282.398
Variance22295.11052
MonotonicityNot monotonic
2022-04-24T23:07:31.925301image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=44)
ValueCountFrequency (%)
2485806
30.5%
493596
18.9%
2863484
18.3%
3981809
 
9.5%
3481502
 
7.9%
3251455
 
7.6%
300384
 
2.0%
283217
 
1.1%
488
 
< 0.1%
3465
 
< 0.1%
Other values (34)64
 
0.3%
(Missing)696
 
3.7%
ValueCountFrequency (%)
01
 
< 0.1%
0.3981
 
< 0.1%
92
 
< 0.1%
194
 
< 0.1%
281
 
< 0.1%
302
 
< 0.1%
382
 
< 0.1%
488
 
< 0.1%
493596
18.9%
942
 
< 0.1%
ValueCountFrequency (%)
36982
< 0.1%
33984
< 0.1%
33482
< 0.1%
33251
 
< 0.1%
32864
< 0.1%
32484
< 0.1%
30491
 
< 0.1%
28361
 
< 0.1%
24831
 
< 0.1%
24481
 
< 0.1%

Filler
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing19026
Missing (%)100.0%
Memory size148.8 KiB

Recipient
Real number (ℝ≥0)

MISSING

Distinct49
Distinct (%)0.3%
Missing655
Missing (%)3.4%
Infinite0
Infinite (%)0.0%
Mean327.8369501
Minimum3.61
Maximum4243
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size148.8 KiB
2022-04-24T23:07:32.074862image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum3.61
5-th percentile94
Q1318
median346
Q3368
95-th percentile369
Maximum4243
Range4239.39
Interquartile range (IQR)50

Descriptive statistics

Standard deviation180.7938518
Coefficient of variation (CV)0.5514749077
Kurtosis254.3737228
Mean327.8369501
Median Absolute Deviation (MAD)22
Skewness14.55732574
Sum6022692.61
Variance32686.41685
MonotonicityNot monotonic
2022-04-24T23:07:32.216696image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
3612639
13.9%
3692587
13.6%
3682553
13.4%
3242549
13.4%
3182504
13.2%
3462231
11.7%
2172146
11.3%
94970
 
5.1%
34825
 
0.1%
31917
 
0.1%
Other values (39)150
 
0.8%
(Missing)655
 
3.4%
ValueCountFrequency (%)
3.611
 
< 0.1%
91
 
< 0.1%
271
 
< 0.1%
311
 
< 0.1%
381
 
< 0.1%
496
 
< 0.1%
94970
5.1%
1024
 
< 0.1%
1491
 
< 0.1%
2172146
11.3%
ValueCountFrequency (%)
42431
 
< 0.1%
39691
 
< 0.1%
36994
< 0.1%
36682
 
< 0.1%
36615
< 0.1%
36241
 
< 0.1%
36132
 
< 0.1%
34831
 
< 0.1%
34693
< 0.1%
33683
< 0.1%

Number_of_Case
Categorical

HIGH CARDINALITY
HIGH CORRELATION
MISSING

Distinct62
Distinct (%)4.7%
Missing17709
Missing (%)93.1%
Memory size630.5 KiB
1
 
91
4
 
60
2
 
57
9
 
56
10
 
54
Other values (57)
999 

Length

Max length4
Median length2
Mean length1.639331815
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique8 ?
Unique (%)0.6%

Sample

1st row1
2nd row4
3rd row10
4th row10
5th row1

Common Values

ValueCountFrequency (%)
191
 
0.5%
460
 
0.3%
257
 
0.3%
956
 
0.3%
1054
 
0.3%
548
 
0.3%
1444
 
0.2%
744
 
0.2%
342
 
0.2%
642
 
0.2%
Other values (52)779
 
4.1%
(Missing)17709
93.1%

Length

2022-04-24T23:07:32.355823image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
191
 
6.9%
460
 
4.6%
257
 
4.3%
956
 
4.3%
1054
 
4.1%
548
 
3.6%
1444
 
3.3%
744
 
3.3%
342
 
3.2%
642
 
3.2%
Other values (52)779
59.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Number_of_Boxes
Categorical

HIGH CARDINALITY
HIGH CORRELATION
MISSING

Distinct64
Distinct (%)0.5%
Missing5356
Missing (%)28.2%
Memory size980.2 KiB
1
1674 
2
1644 
3
1576 
4
1305 
5
1085 
Other values (59)
6386 

Length

Max length4
Median length1
Mean length1.267739576
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique9 ?
Unique (%)0.1%

Sample

1st row5
2nd row15
3rd row8
4th row1
5th row4

Common Values

ValueCountFrequency (%)
11674
 
8.8%
21644
 
8.6%
31576
 
8.3%
41305
 
6.9%
51085
 
5.7%
6888
 
4.7%
7738
 
3.9%
8628
 
3.3%
10566
 
3.0%
9476
 
2.5%
Other values (54)3090
16.2%
(Missing)5356
28.2%

Length

2022-04-24T23:07:32.483398image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
11674
12.2%
21644
12.0%
31576
11.5%
41305
9.5%
51085
 
7.9%
6889
 
6.5%
7738
 
5.4%
8628
 
4.6%
10566
 
4.1%
9476
 
3.5%
Other values (53)3089
22.6%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Sintered_State
Categorical

HIGH CORRELATION
MISSING

Distinct22
Distinct (%)2.8%
Missing18247
Missing (%)95.9%
Memory size617.4 KiB
1
683 
6
 
21
2
 
16
3
 
10
7
 
8
Other values (17)
 
41

Length

Max length3
Median length1
Mean length1.037227214
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5 ?
Unique (%)0.6%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1683
 
3.6%
621
 
0.1%
216
 
0.1%
310
 
0.1%
78
 
< 0.1%
96
 
< 0.1%
194
 
< 0.1%
54
 
< 0.1%
163
 
< 0.1%
173
 
< 0.1%
Other values (12)21
 
0.1%
(Missing)18247
95.9%

Length

2022-04-24T23:07:32.607756image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
1683
87.7%
621
 
2.7%
216
 
2.1%
310
 
1.3%
78
 
1.0%
96
 
0.8%
194
 
0.5%
54
 
0.5%
173
 
0.4%
163
 
0.4%
Other values (12)21
 
2.7%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Working_Hours
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct97
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.72711027
Minimum0
Maximum12
Zeros61
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size148.8 KiB
2022-04-24T23:07:32.739921image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.5
Q11.666666667
median4
Q34.5
95-th percentile8
Maximum12
Range12
Interquartile range (IQR)2.833333333

Descriptive statistics

Standard deviation2.447716131
Coefficient of variation (CV)0.6567329522
Kurtosis-0.7629773019
Mean3.72711027
Median Absolute Deviation (MAD)2
Skewness0.5674176199
Sum70912
Variance5.991314257
MonotonicityNot monotonic
2022-04-24T23:07:32.889703image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
44480
23.5%
83277
17.2%
11105
 
5.8%
2954
 
5.0%
3785
 
4.1%
1.5763
 
4.0%
2.5591
 
3.1%
0.5574
 
3.0%
3.5376
 
2.0%
0.666666667310
 
1.6%
Other values (87)5811
30.5%
ValueCountFrequency (%)
061
 
0.3%
0.08333333393
 
0.5%
0.166666667198
 
1.0%
0.2551
 
0.3%
0.333333333174
 
0.9%
0.41666666751
 
0.3%
0.5574
3.0%
0.58333333342
 
0.2%
0.666666667310
1.6%
0.7585
 
0.4%
ValueCountFrequency (%)
124
 
< 0.1%
9.51
 
< 0.1%
83277
17.2%
7.8333333331
 
< 0.1%
7.752
 
< 0.1%
7.66666666712
 
0.1%
7.5833333332
 
< 0.1%
7.552
 
0.3%
7.4166666671
 
< 0.1%
7.33333333331
 
0.2%

Total_Working_Hours
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct97
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean223.6266162
Minimum0
Maximum720
Zeros61
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size148.8 KiB
2022-04-24T23:07:33.036314image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile30
Q1100
median240
Q3270
95-th percentile480
Maximum720
Range720
Interquartile range (IQR)170

Descriptive statistics

Standard deviation146.8629679
Coefficient of variation (CV)0.6567329522
Kurtosis-0.7629773019
Mean223.6266162
Median Absolute Deviation (MAD)120
Skewness0.5674176199
Sum4254720
Variance21568.73133
MonotonicityNot monotonic
2022-04-24T23:07:33.180971image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2404480
23.5%
4803277
17.2%
601105
 
5.8%
120954
 
5.0%
180785
 
4.1%
90763
 
4.0%
150591
 
3.1%
30574
 
3.0%
210376
 
2.0%
40310
 
1.6%
Other values (87)5811
30.5%
ValueCountFrequency (%)
061
 
0.3%
593
 
0.5%
10198
 
1.0%
1551
 
0.3%
20174
 
0.9%
2551
 
0.3%
30574
3.0%
3542
 
0.2%
40310
1.6%
4585
 
0.4%
ValueCountFrequency (%)
7204
 
< 0.1%
5701
 
< 0.1%
4803277
17.2%
4701
 
< 0.1%
4652
 
< 0.1%
46012
 
0.1%
4552
 
< 0.1%
45052
 
0.3%
4451
 
< 0.1%
44031
 
0.2%

Interactions

2022-04-24T23:07:23.478078image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-24T23:06:59.624026image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-24T23:07:01.525912image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-24T23:07:03.394861image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-24T23:07:05.198206image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-24T23:07:07.202542image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-24T23:07:08.976901image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-24T23:07:10.894524image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-24T23:07:12.668367image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-24T23:07:14.537703image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-24T23:07:15.985825image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-24T23:07:17.832275image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-24T23:07:19.731594image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-24T23:07:21.625774image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-24T23:07:23.620188image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-24T23:06:59.766201image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-24T23:07:01.662578image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-24T23:07:03.533888image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-24T23:07:05.339793image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-24T23:07:07.334185image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-24T23:07:09.116488image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-24T23:07:11.034158image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-24T23:07:12.809985image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-24T23:07:14.649619image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-24T23:07:16.117479image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-24T23:07:17.978950image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-24T23:07:19.867195image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-24T23:07:21.762417image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-24T23:07:23.749876image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-24T23:06:59.909853image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-24T23:07:01.794192image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-24T23:07:03.666129image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-24T23:07:05.475601image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-24T23:07:07.455865image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-24T23:07:09.243189image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-24T23:07:11.164886image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-24T23:07:12.920549image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-24T23:07:14.745582image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-24T23:07:16.236161image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-24T23:07:18.114553image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
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2022-04-24T23:07:05.617788image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-24T23:07:07.581880image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-24T23:07:09.374877image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-24T23:07:11.293473image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-24T23:07:13.032216image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-24T23:07:14.840328image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-24T23:07:16.361183image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-24T23:07:18.249157image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-24T23:07:20.122554image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
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2022-04-24T23:07:02.168547image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-24T23:07:03.944386image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
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2022-04-24T23:07:07.717830image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-24T23:07:09.514552image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-24T23:07:11.428540image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-24T23:07:13.151934image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-24T23:07:14.951049image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-24T23:07:16.479373image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-24T23:07:18.395766image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
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2022-04-24T23:07:24.161778image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
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2022-04-24T23:07:04.070051image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
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2022-04-24T23:07:07.838904image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-24T23:07:09.745616image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-24T23:07:11.558327image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
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2022-04-24T23:07:20.379865image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-24T23:07:22.309705image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-24T23:07:24.296521image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-24T23:07:00.472487image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
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2022-04-24T23:07:04.201736image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
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2022-04-24T23:07:07.969555image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-24T23:07:09.872748image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
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2022-04-24T23:07:13.506920image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-24T23:07:15.160489image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-24T23:07:16.731658image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
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2022-04-24T23:07:20.504491image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-24T23:07:22.439664image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-24T23:07:24.421675image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
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2022-04-24T23:07:06.279061image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
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2022-04-24T23:07:22.571820image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
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2022-04-24T23:07:02.650259image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-24T23:07:04.434113image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-24T23:07:06.400917image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-24T23:07:08.222913image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-24T23:07:10.126068image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-24T23:07:11.914337image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-24T23:07:13.748235image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-24T23:07:15.352015image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
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2022-04-24T23:07:15.452707image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
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Correlations

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Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-04-24T23:07:33.751404image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-04-24T23:07:34.067592image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-04-24T23:07:34.384710image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

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A simple visualization of nullity by column.
2022-04-24T23:07:26.635237image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2022-04-24T23:07:27.108157image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
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The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

DataShiftToolMaterial_TypeMaterialProduct CodeBatch_IDtemperatureSpeedquenchrateRapid dewaxingweight_kgunit_weight_gQtyProcess_TimeWaiting_TimeHolding_TimeChange_Temperature_TimeFurnace_Maintenance_TimeForm_NumberAffirmantFillerRecipientNumber_of_CaseNumber_of_BoxesSintered_StateWorking_HoursTotal_Working_Hours
01/1C1NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN1.000.000000NaN90.0NaNNaNNaNNaN325.0NaN369.0NaNNaNNaN1.50000090
11/1C11.0256808201230A1120.060.0NaNNaNNaN90.08.4510650.887570390.0NaNNaNNaNNaNNaN325.0NaN369.0NaN5NaN6.500000390
21/1A1NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN1.000.000000NaN60.0NaNNaNNaNNaN49.0NaN346.0NaNNaNNaN1.00000060
31/1A11.015-18005201231A1120.060.0NaNNaNNaN300.0140.002142.857143420.0NaNNaNNaNNaNNaN498.0NaN368.0NaN15NaN7.000000420
41/1A11.015-18005201231A1120.060.0NaNNaNNaN147.5140.001053.571429200.0NaNNaNNaNNaNNaN248.0NaN368.0NaN8NaN3.333333200
51/1A1NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN1.000.000000NaN40.0NaNNaNNaNNaN248.0NaN368.0NaNNaNNaN0.66666740
61/1C1NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN1.000.000000NaN240.0NaNNaNNaNNaN248.0NaN368.0NaNNaNNaN4.000000240
71/1C21.05-74454201230A1120.060.0NaNNaNNaN15.4138.00111.59420360.0NaNNaNNaNNaNNaN325.0NaN369.0NaN1NaN1.00000060
81/1C21.05-34666201231A1120.060.0NaNNaNNaN80.057.201398.601399420.0NaNNaNNaNNaNNaN325.0NaN369.0NaN4NaN7.000000420
91/1A21.05-34666201231A1120.060.0NaNNaNNaN70.057.201223.776224270.0NaNNaNNaNNaNNaN49.0NaN368.0NaN4NaN4.500000270

Last rows

DataShiftToolMaterial_TypeMaterialProduct CodeBatch_IDtemperatureSpeedquenchrateRapid dewaxingweight_kgunit_weight_gQtyProcess_TimeWaiting_TimeHolding_TimeChange_Temperature_TimeFurnace_Maintenance_TimeForm_NumberAffirmantFillerRecipientNumber_of_CaseNumber_of_BoxesSintered_StateWorking_HoursTotal_Working_Hours
1901612/31C81.08-26556211224A1120.060.0NaNNaNNaN387.8128.253023.781676480.0NaNNaNNaNNaN535.049.0NaN318.0NaN18NaN8.000000480
1901712/31A81.08-26556211224A1120.060.0NaNNaNNaN409.3128.253191.423002385.0NaNNaNNaNNaN535.0286.0NaN324.0NaN19NaN6.416667385
1901812/31A81.020-26534NaN1120.060.0NaNNaNNaN92.1209.00440.66985795.0NaNNaNNaNNaN535.0286.0NaN324.0NaN15NaN1.58333395
1901912/31A81.015-43394211227A1120.060.0NaNNaNNaN200.0308.00649.350649240.0NaNNaNNaNNaN535.0248.0NaN324.0NaN10NaN4.000000240
1902012/31C81.015-43394211227A1120.060.0NaNNaNNaN200.0308.00649.350649240.0NaNNaNNaNNaN535.0248.0NaN369.0NaN10NaN4.000000240
1902112/31C9NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN1.000.000000NaN480.0NaNNaNNaN96.049.0NaN361.0NaNNaNNaN8.000000480
1902212/31A9NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN1.000.000000NaN480.0NaNNaNNaN96.0286.0NaN346.0NaNNaNNaN8.000000480
1902312/31A9NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN1.000.000000NaN210.0NaNNaNNaN96.0248.0NaN346.0NaNNaNNaN3.500000210
1902412/31A91.0173609211229C800.050.0NaNNaNNaNNaN28.500.00000030.0NaNNaNNaNNaN96.0248.0NaN393.0NaNNaNNaN0.50000030
1902512/31C91.0173609211229C800.050.0NaNNaNNaN120.028.504210.526316240.0NaNNaNNaNNaN96.0248.0NaN217.0NaN6NaN4.000000240